Thermal stress risk assessment using body worn sensors

ABSTRACT

Methods, systems and computer program products provide for a Risk Assessment Engine that receives body ambient temperature data captured by a sensor in contact with a person. The Risk Assessment Engine characterizes types of activities performed by the person during a time range associated with the body ambient temperature data. The Risk Assessment Engine determines a risk classification individualized for the person based on respective workloads and the corresponding allocations of work and rest experienced by the person during performance of the characterized types of activities.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/813,595, filed Mar. 4, 2019, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to receiving sensor data andmore specifically to identifying risks based on the sensor data.

BACKGROUND

The subject matter discussed in the background section should not beassumed to be prior art merely as a result of its mention in thebackground section. Similarly, a problem mentioned in the backgroundsection or associated with the subject matter of the background sectionshould not be assumed to have been previously recognized in the priorart. The subject matter in the background section merely representsdifferent approaches, which in and of themselves may also be inventions.

In conventional systems, wet bulb globe temperature (“Wbgt”) devices arenormally handheld devices that do not lend themselves to being worn onthe body. Wbgt devices provide a quantified metric that captures all theparameters that impact the body's ability to maintain a safe core bodytemperature through evaporation. Key parameters that impact heat stressinclude temperature, humidity, pressure and radiant energy.

SUMMARY

Embodiments disclosed herein define a body ambient temperaturemeasurement as a quantitative metric of temperature in close proximityto the skin obtained via one or more body worn sensors. The body ambienttemperature replaces the Wbgt in its application to temperaturestandards that indicate, for example, that a climatized individualperforming a job with a 60% allocation of work with a heavy workload,must not exceed a Wbgt of 27.5 degrees Celsius to remain safe and not bethermally at risk. The body ambient temperature accounts fortemperature, humidity, pressure, and radiant energy. Unlike the Wbgt,body ambient temperature provides an advantage of not requiring aclothing adjustment factor.

A body sensor may be worn underneath, for example, the outermost layerof clothing to capturing a person's body ambient temperature. To providea better representation of the skin temperature across the entire upperbody, an array of body sensors could be worn on the upper body. Aweighted average across the readings of all the worn sensors may therebyperformed to calculate body ambient temperature value at any given time.

The disclosed embodiments herein are generally directed to a RiskAssessment Engine. A risk assessment represents a thermal stress risk ata given time—or period of time—during which a person performs varioustypes of activities. The risk assessment is based on the type ofactivities performed by the person which are represented, in part, byraw data captured by a sensor(s) worn by the person. The Risk AssessmentEngine characterizes the person's activities according to pre-definedactivities. The pre-defined activities are associated with a workloadcategory, such as light, medium/moderate, heavy and very heavy.

In addition, the Risk Assessment Engine determines the person'sallocation of work, which represents a percentage of time within a timeperiod (such as an hour) that the person is engaged in activities of acertain type(s) of workload or at rest. Body ambient temperaturecaptured by the sensor(s) expectedly increases when a person's workallocation of activities favors more activities over rest and/or theperson is performing activities with heavy to very heavy workloads—asopposed to medium to light workloads.

Safety threshold temperatures are predetermined for activities ofvarious workload categories with respect to various allocation of workranges, such as allocation of work range 0%-25% and 26%-50%, where 50%indicates that a person engages in activities as often as the personrests. An allocation of work at 100% represents a person who has norest, and such an 100% allocation of work would certainly be corelatedwith an increase in body ambient temperature. The higher one's bodyambient temperature increases with respect to an allocation of work thatincludes certain types of heavy to very heavy workloads, thepredetermined safety threshold temperature necessary to avoid thermalstress risk inevitably decreases. In contrast, as one's body ambienttemperature decreases with respect to a more restful allocation of workthat includes certain types of activities that carry lighter workloads,the predetermined safety threshold temperatures necessary to avoidthermal stress risk inevitably increases. In other words, a more restedperson engaged in lighter workloads is less likely to be exposed to arisk of thermal stress until their body ambient temperature exceeds ahigher predetermined safety threshold temperature. However, if thatperson was engaged in the lighter workloads in an environment thatincluded very warm outdoor temperatures, then that person's body ambienttemperature may still pass the higher predetermined safety thresholdtemperature regardless of their lighter workload and restful allocationof work.

The Risk Assessment Engine automatically and continuously quantifiesthermal risk assessments based on direct sensor measurements of aperson's body dynamics and surrounding environment. A sensor(s) worn bya person performs continuous data capture and the captured raw data(body ambient temperature data) may be sent to a cloud computinginfrastructure that hosts the Risk Assessment Engine. Upon receipt bythe cloud computing infrastructure, the Risk Assessment Engine performsa sequence of data trimming, data synchronization, data transformationand pattern recognition techniques to identify one or more pre-definedactivities indicated in part by the captured raw data (body ambienttemperature data).

The Risk Assessment Engine obtains a workload of each pre-definedactivity identified in the captured raw data (body ambient temperaturedata). In one embodiment, the workload may be defined by a metabolicrate in units of watts that is experienced during performance of apre-defined activity identified in the captured raw data. In addition,an allocation of work during a time range during which the identifiedpre-defined activity was performed is additional determined. In oneembodiment, the allocation of work may be a ratio of work time and resttime that occurred when the person performed the identified pre-definedactivity. Given the workload and the allocation of work experienced byperson while performing the identified pre-defined activity, the RiskAssessment Engine determines a risk classification representing theperson's degree of thermal risk exposure. It is understood that riskclassification for any number of persons performing one or morepre-defined activities can be graphically represented in a graphicaluser interface dashboard associated with Risk Assessment Engine.

The disclosed embodiments generally include a method and computerprogram product for the Risk Assessment Engine. The Risk AssessmentEngine receives body ambient temperature data captured by a sensor incontact with a person. The Risk Assessment Engine characterizes types ofactivities performed by the person during a time range associated withthe body ambient temperature data. The Risk Assessment Engine determinesa risk classification (such as a thermal risk classification)individualized for the person based on respective workloads and thecorresponding allocations of work and rest experienced by the personduring performance of the characterized types of activities.

The disclosed embodiments may also include a system for a RiskAssessment Engine. The system may include one or more processors; and anon-transitory computer readable medium storing a plurality ofinstructions, which when executed, cause the one or more processors to:receive body ambient temperature data captured by a sensor in contactwith a person, characterize types of activities performed by the personduring a time range associated with the body ambient temperature dataand determine a risk classification individualized for the person basedon respective workloads and the corresponding allocations of work andrest experienced by the person during performance of the characterizedtypes of activities.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following drawings like reference numbers are used to refer tolike elements. Although the following figures depict various examples,the one or more implementations are not limited to the examples depictedin the figures.

FIG. 1 is a high-level diagram of an environment for determining a riskassessment, in an embodiment;

FIG. 2 is a high-level diagram of an exemplary graphical user interfacedashboard, in an embodiment;

FIG. 3 is an operational flow diagram illustrating a high-level overviewof a method for determining a risk assessment, in an embodiment;

FIG. 4 is a high-level diagram of an exemplary data flow for determininga risk assessment, in an embodiment;

FIG. 5 shows a diagram of an example computing system that may be usedwith some embodiments; and

FIG. 6 shows a diagram of an example network environment that may beused with some embodiments.

DETAILED DESCRIPTION

In accordance with embodiments described herein, there are providedmethods, systems and computer program products for a Risk AssessmentEngine that determines a risk classification individualized for aperson(s) based on the types of activities the person(s) performs whilewearing a sensor(s).

Any of the embodiments described herein may be used alone or togetherwith one another in any combination. The one or more implementationsencompassed within this specification may also include embodiments thatare only partially mentioned or alluded to or are not mentioned oralluded to at all in the abstract. Although various embodiments may havebeen motivated by various deficiencies with the prior art, which may bediscussed or alluded to in one or more places in the specification, theembodiments do not necessarily address any of these deficiencies. Inother words, different embodiments may address different deficienciesthat may be discussed in the specification. Some embodiments may onlypartially address some deficiencies or just one deficiency that may bediscussed in the specification, and some embodiments may not address anyof these deficiencies.

Some embodiments described herein may be described in the generalcontext of computing system executable instructions, such as programmodules, being executed by a computer. Generally, program modulesinclude routines, programs, objects, components, data structures, etc.that performs particular tasks or implement particular abstract datatypes. Those skilled in the art can implement the description and/orfigures herein as computer-executable instructions, which can beembodied on any form of computing machine program product discussedbelow.

Some embodiments may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

As shown in FIG. 1, a plurality of employees 102 each wear one or moresensors 100 to form a connected workforce. The sensors continuouslymeasure each employee's body ambient temperature as well as motion datarepresentative of their various activities during a work shift. Thesensors may also detect conditions surrounding each employee, such asoutdoor and indoor temperature. The raw data captured by each sensorworn by the respective employees is continuously uploaded to a RiskAssessment Engine 106 that is hosted within a cloud computinginfrastructure 104. The Risk Assessment Engine 106 outputs data to arisk analytics dashboard 108, which provides various graphs and datatables representing data characteristics related to each employee'sthermal stress risk. For example, each employee may be associated withtheir own dashboard and an enterprise-wide analytics dashboard system108 provides access to each employee-dedicated dashboard.

The Risk Assessment Engine 106 includes a plurality of modules forexecution of the operations, steps, methods, actions and calculationsdescribed herein. The modules may include computer hardware and softwareinstructions provided in a non-transitory executable medium. The RiskAssessment Engine 106 includes a data receipt module 106-1 for receivingraw data captured from a plurality of sensors 100 associated with aplurality of peoples, such as multiple employees 102 of a connectedworkforce. The Risk Assessment Engine 106 includes a trim module 106-2for trimming the raw data from the sensors and external data fromexternal data sources. In various embodiments, data may be trimmed inorder to discard data associated with timestamps that occur outside of arelevant time period, such as a relevant work shift of an employee. TheRisk Assessment Engine 106 includes a synchronization module 106-3 thatpairs trimmed raw data and trimmed external data based on matchingtimestamp data. The Risk Assessment Engine 106 includes a transformationmodule 106-4 that increases data accuracy, reduce data errors andperform data conversions.

The Risk Assessment Engine 106 includes a pattern recognition module106-5 that identifies characteristics in transformed data that indicatesthe employee engaged in (or is engaging in) an activity that is similarto a pre-defined activity. By characterizing a portion of the employee'stransformed data as an appropriate pre-defined activity, the RiskAssessment Engine 106 can model the employee's workload and allocationof work. The Risk Assessment Engine 106 includes a risk module 106-6that determines a risk assessment based in part on the employee'sworkload, allocation of work and body ambient temperature data capturedby one or more sensors. The Risk Assessment Engine 106 includes adashboard module 106-7 that generates graphical rendering data based onthe employee's workload, allocation of work and body ambient temperaturedata.

As shown in FIG. 2, a graphical user interface dashboard 200 for a RiskAssessment Engine 106 is illustrated. The dashboard 200 may beassociated with body ambient temperature data captured by a sensor(s) incontact with an employee. The dashboard 200 includes a workloadstatistics table 202. The workload statistics table 202 provides a listof workload categories (rest, light, medium, heavy, very heavy) and apercentage of time the employee has engaged in work tasks that arecategorized according to the workload categories. The dashboard 200includes a risky events graph 202. The risky events graph 204 displaysone or more lines of color contrast 206 to represent a reference time(s)at which the body ambient temperature data of the employee is above athreshold temperature for the type of activity performed by the employeeat the same reference time.

Pre-determined threshold temperature values may be defined for eachworkload category (light, medium/moderate, heavy, very heavy) within apercentage range of allocation of work. If the allocation of work isbetween 50%-75%, then there will be threshold temperature values foreach workload category. If the allocation of work is between 0%-25%,then there will be other threshold temperature values for each workloadcategory. For example, if a person's allocation of work is between50%-75% and that person is performing a first characterized type ofactivity that results in a medium/moderate workload, a first thresholdtemperature value may be 29 degrees. However, if the person's allocationof work is between 0%-25% and that person is performing a secondcharacterized type of activity that also results in a medium/moderateworkload, a second threshold temperature value may be 31.5 degrees. Asshown by the exemplary first and second threshold temperature values, aperson experiencing a lower extent of allocation of work between 0%-25%will not incur a thermal stress risk until the employee's body ambienttemperature data goes above 31.5 degree. However, a higher range ofworkload allocation (50%-75%) inevitably results in a thermal stressrisk at a lower 29 degrees. Therefore, the risky events graph 204 willpresent one or more lines of color contrast 206 when an employee's bodyambient temperature data is above the threshold temperature value thatis appropriate for the employee's allocation of work percentage andworkload category for the type of activity the employee performed.

The dashboard 200 includes a temperature graph 208 which graphs theemployee's body ambient temperature 210 and threshold temperature 212 asa function of time. The temperature graph 208 may also include a cursor214 that highlights a comparison of an employee's body ambienttemperature to the appropriate threshold temperature at a particularpoint in time.

Graph portion 216 represents a point of time at which an employee's workshift begins, which results in fast increase in body ambienttemperature. Graph portion 218 represents a drop in the employee's bodyambient temperature as the employee's workload decreases. Therefore, thethreshold temperature rapidly increases because as the employee's workallocation decreases, the employee can afford to experience a higherbody ambient temperature before being put at risk of thermal exposure.Graph portion 220 shows climbing ambient body temperature even thoughthe work allocation stays at a lower percentage. This indicates that theemployee's body ambient temperature has increased due to sun exposureand not as a result of the strain of the employee's workload. Graphportion 222 shows that the threshold temperature remains relatively lowand constant as both the allocation of work and workload remain highwhile the employee is still performing activities outdoors. It isunderstood that in various embodiments, the table 202 and graphs 204,208 are rendered in real-time as the employee is engaged in one or moretasks and activities.

As shown in FIG. 3, an operational flow diagram 300 includes step 302 atwhich the Risk Assessment Engine 106 receiving body ambient temperaturedata captured by a sensor in contact with a person. For example, thebody ambient temperature data may be timestamped data that representsbody segment motion, body temperature, heart rate, galvanic skinresponse (GSR), electromyograms (EMG) and environmental conditions data,such as humidity, pressure and positional information associated with aglobal navigation satellite system.

At step 304, the Risk Assessment Engine 106 characterizes one or moretypes of activities performed by the person during a time rangeassociated with the body ambient temperature data. The Risk AssessmentEngine 106 identifies external data related to a geographic location ofthe person. The Risk Assessment Engine 106 trims the start and end ofthe raw captured body ambient temperature data to result in trimmed bodyambient temperature data that encompasses a time period that is relevantto risk assessment, such as the work shift of the person. The RiskAssessment Engine 106 synchronizes the trimmed body ambient temperaturedata with external data. Synchronization converts the multiple,parallel, asynchronously timestamped raw sensor data in the trimmed bodyambient temperature data to fully synchronous data using variousinterpolation techniques.

The Risk Assessment Engine 106 employs a transformation process to thesynchronized data in order to reduce errors and increase data accuracy.For example, there may be multiple transformation modules associatedwith the Risk Assessment Engine 106, where each module operates inparallel and independently from other transformation modules. Variousexemplary transformation modules may be based on methods and techniquessimilar to those described in U.S. Pat. No. 6,820,025 (“Method andapparatus for motion tracking of an articulated rigid body”). Variousexemplary transformation modules may increase the accuracy ofthree-dimensional positional information by combining global navigationsatellite system data, accelerometer data, local ambient pressure dataand altitude data from external data sources. Various exemplarytransformation modules may also convert sensor motion data to determinethe person's caloric consumption.

Once the data is transformed, the Risk Assessment Engine 106 employs anactivity characterization process. Activity characterization takes asinput the transformed data and performs one or more pattern recognitiontechniques on the transformed data in order to categorize thetransformed data according to similar pre-defined activities. Thepattern recognition techniques detect a portion(s) of the transformeddata that is indicative of data characteristics expected to be observedduring performance of a pre-defined activity, such as sitting, climbingstairs or carrying an object. The Risk Assessment Engine 106 furtherdefines start and end times for each characterized pre-defined activitydetected in the transformed data.

At step 306, the Risk Assessment Engine 106 determines a riskclassification individualized for the person based at least on one ormore respective workloads and the corresponding allocations of work andrest experienced by the person during performance of the characterizedtypes of activities. For example, risk classification by the RiskAssessment Engine 106 may be automated and scale thermal risk analysisof guidelines published by the American Conference of GovernmentalHygienists (ACGIH) through digitization of input parameters. The inputparameters may be climatization, workloads, allocation of work and wetbulb globe temperature (Wbgt). According to the ACGIH, for a givenclimatization, workload and work duty cycle, if the measured Wbgtexceeds a specified numerical value then a risk of thermal exposure ispresent. For example, the ACGIH standards indicate that a climatizedindividual performing a job with a 60% allocation of work with a heavyworkload, must not exceed a Wbgt of 27.5 degrees Celsius to remain safeand not be thermally at risk.

According to various embodiments, new employees or mature employees(such as employees over 55 years of age) working in a constantlychanging environment will be classified as “unacclimatized.” The“unacclimatized” classification can automatically be determined by thetracking of an employee's daily average Wbgt exposure over the prior 2weeks on a daily basis. Large changes measured in the daily average Wbgtwill classify the climatization to be unacclimatized. Conversely,consistent Wbgt will classify the employee to be acclimatized.Alternatively, the classification could be manually chosen by a properlytrained individual should historical data not be present.

Workload is defined for the Risk Assessment Engine 106 by a metabolicrate (MR) in units of watts experienced during performance of tasks andactivities that correspond to various workload categories. The metabolicrate can be further refined on an individualized level according to theparticular person's measured body weight. The workload can beautomatically be determined by having one or more motion sensors worn onthe body of the person. Motion data from the motion sensors furtherallow for the recreation by the Risk Assessment Engine 106 of theperson's physical motions that occurred during performance of varioustasks and activities. Such recreation of physical motions enables theRisk Assessment Engine 106 to determine a proper characterization of theperson's tasks and activities. Therefore, with proper activitycharacterization in relation to pre-defined activities and knowledge ofthe person's weight, a metabolic rate estimate and workload category canbe established for any given moment in time. In some embodiments, themetabolic rate estimation may be further enhanced with the addition of aheart rate monitor.

Allocation of work for the Risk Assessment Engine 106 is derivedaccording to a historical sliding window of one hour, during which apercentage of time spent at rest vs. not at rest (i.e. light,medium/moderate, heavy, very heavy) is calculated. Risk Classificationis a means of representing the degree of thermal risk exposure. Fourclassifications levels are defined to represent an increasing degree ofrisk of exposure. In some embodiments, the Risk Assessment Engine 106re-assessed thermal risk periodically (e.g. every 30 seconds) so thatthe changes in thermal risk exposure are promptly detected.

As shown in FIG. 4, the Risk Assessment Engine 106 utilizes a data flow400 on an individual level and may further refine the data flow to beapplied for a various day. For example, the Risk Assessment Engine 106may separately utilize the data flow 400 with respect to a firstemployee (“User N”) 402 and a second employee (“User 1”) 404. The RiskAssessment Engine 106 further refines the data flow 400 for the secondemployee 404 separately for distinct days 406, 408 (or for distinct workshifts that occurred on different days).

To determine a thermal risk assessment for an employee 404 on aparticular day 408, the data flow 400 includes receipt 410 of raw data420 captured from one or more sensors worn by the employee 404. The rawdata 420 may include body segment motion data, body temperature data,heart rate data, galvanic skin response (GSR) data, electromyograms(EMG) data and environmental conditions data comprising at least one of:humidity, pressure and positional information associated with a globalnavigation satellite system. In response to receipt 410 of the raw data420, the data flow 400 identifies external data from an external datasource(s) 422 with external data related to a geographic location of theemployee 404.

The data flow 400 includes raw translation 412 in which transformed datais generated. The transformed data is based on body ambient temperaturedata from the received raw data 420 that has been synchronized with theexternal data to represent risk conditions associated with one or moreactivities performed by the employee 404. To generate the transformeddata, the data flow 400 includes trimming the body ambient temperaturedata and the external data to identify data contemporaneous totimestamps of the raw data 420. The data flow 400 includes generatingsynchronized data based on synchronizing the trimmed external data andthe trimmed body ambient temperature data.

The data flow 400 further includes transforming the synchronized data.For example, physical orientation data of the body ambient temperaturedata may be transformed to represent one or more body motions.Positional information of the body ambient temperature data may becombined with at least one of: accelerometer data, local ambientpressure from the external data, altitude data from the external dataand motion data from the body ambient temperature data in order togenerate data representing a relationship between physical energy burnand caloric consumption of the employee 404.

The data flow 400 includes adapted ACGIH analysis 414 during which oneor more types of activities performed by the employee 404 arecharacterized. The data flow includes providing input transformed datato one or more pattern recognition techniques to transformed data. Theone or more pattern recognition techniques match at least one or moresegments of time associated with the transformed data that is indicativeof a time epoch of a performed pre-defined activity available from aplurality of pre-defined activities. The plurality of pre-definedactivities comprises, for example: lifting an object, pushing an object,pulling an object, carrying an object, walking, running, standing,sitting, sitting in a moving vehicle and climbing stairs. It isunderstood that embodiments of the Risk Assessment are not limited toACGIH standards.

The data flow 400 further includes risk classification 416. Riskclassification 416 includes identifying respective workloads of theemployee 404 associated with the one or more types of characterizedactivities performed by the employee 404. According to variousembodiments, each respective workload may be based on an establishedmetabolic rate experienced during a pre-defined activity that matches arespective activity represented in part by the body ambient temperaturedata. A respective allocation of work and rest describes a ratio of restand non-rest during an interval of time in which a particular type ofworkload was experienced by the employee 404.

The data flow 400 also includes providing data for display via anindividual summary dashboard, such as dashboard 200 described above withrespect to FIG. 2.

System Overview

Referring to FIG. 5, the computing system 502 may include, but are notlimited to, a processing unit 520 having one or more processing cores, asystem memory 530, and a system bus 521 that couples various systemcomponents including the system memory 530 to the processing unit 520.The system bus 521 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. By way ofexample, and not limitation, such architectures include IndustryStandard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus,Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA)locale bus, and Peripheral Component Interconnect (PCI) bus also knownas Mezzanine bus.

The computing system 502 typically includes a variety of computerprogram product. Computer program product can be any available mediathat can be accessed by computing system 502 and includes both volatileand nonvolatile media, removable and non-removable media. By way ofexample, and not limitation, computer program product may storeinformation such as computer readable instructions, data structures,program modules or other data. Computer storage media include, but arenot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium which can be used tostore the desired information and which can be accessed by computingsystem 502. Communication media typically embodies computer readableinstructions, data structures, or program modules.

The system memory 530 may include computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 531and random access memory (RAM) 532. A basic input/output system (BIOS)533, containing the basic routines that help to transfer informationbetween elements within computing system 502, such as during start-up,is typically stored in ROM 531. RAM 532 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 520. By way of example, and notlimitation, FIG. 5 also illustrates operating system 534, applicationprograms 535, other program modules 536, and program data 537.

The computing system 502 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 5 also illustrates a hard disk drive 541 that reads from or writesto non-removable, nonvolatile magnetic media, a magnetic disk drive 551that reads from or writes to a removable, nonvolatile magnetic disk 552,and an optical disk drive 555 that reads from or writes to a removable,nonvolatile optical disk 556 such as, for example, a CD ROM or otheroptical media. Other removable/non-removable, volatile/nonvolatilecomputer storage media that can be used in the exemplary operatingenvironment include, but are not limited to, USB drives and devices,magnetic tape cassettes, flash memory cards, digital versatile disks,digital video tape, solid state RAM, solid state ROM, and the like. Thehard disk drive 541 is typically connected to the system bus 521 througha non-removable memory interface such as interface 540, and magneticdisk drive 551 and optical disk drive 555 are typically connected to thesystem bus 521 by a removable memory interface, such as interface 550.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 5, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputing system 502. In FIG. 5, for example, hard disk drive 541 isillustrated as storing operating system 544, application programs 545,other program modules 546, and program data 547. Note that thesecomponents can either be the same as or different from operating system534, application programs 535, other program modules 536, and programdata 537. The operating system 544, the application programs 545, theother program modules 546, and the program data 547 are given differentnumeric identification here to illustrate that, at a minimum, they aredifferent copies.

A user may enter commands and information into the computing system 502through input devices such as a keyboard 562, a microphone 563, and apointing device 561, such as a mouse, trackball or touch pad or touchscreen. Other input devices (not shown) may include a joystick, gamepad, scanner, or the like. These and other input devices are oftenconnected to the processing unit 520 through a user input interface 560that is coupled with the system bus 521, but may be connected by otherinterface and bus structures, such as a parallel port, game port or auniversal serial bus (USB). A monitor 591 or other type of displaydevice is also connected to the system bus 521 via an interface, such asa video interface 590. In addition to the monitor, computers may alsoinclude other peripheral output devices such as speakers 597 and printer596, which may be connected through an output peripheral interface 590.

The computing system 502 may operate in a networked environment usinglogical connections to one or more remote computers, such as a remotecomputer 580. The remote computer 580 may be a personal computer, ahand-held device, a server, a router, a network PC, a peer device orother common network node, and typically includes many or all of theelements described above relative to the computing system 502. Thelogical connections depicted in

FIG. 5 includes a local area network (LAN) 571 and a wide area network(WAN) 573 but may also include other networks. Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets and the Internet.

When used in a LAN networking environment, the computing system 502 maybe connected to the LAN 571 through a network interface or adapter 570.When used in a WAN networking environment, the computing system 502typically includes a modem 572 or other means for establishingcommunications over the WAN 573, such as the Internet. The modem 572,which may be internal or external, may be connected to the system bus521 via the user-input interface 560, or other appropriate mechanism. Ina networked environment, program modules depicted relative to thecomputing system 502, or portions thereof, may be stored in a remotememory storage device. By way of example, and not limitation, FIG. 5illustrates remote application programs 585 as residing on remotecomputer 580. It will be appreciated that the network connections shownare exemplary and other means of establishing a communications linkbetween the computers may be used.

It should be noted that some embodiments described herein may be carriedout on a computing system such as that described with respect to FIG. 5.However, some embodiments may be carried out on a server, a computerdevoted to message handling, handheld devices, or on a distributedsystem in which different portions of the present design may be carriedout on different parts of the distributed computing system.

Another device that may be coupled with the system bus 521 is a powersupply such as a battery or a Direct Current (DC) power supply) andAlternating Current (AC) adapter circuit. The DC power supply may be abattery, a fuel cell, or similar DC power source needs to be rechargedon a periodic basis. The communication module (or modem) 572 may employa Wireless Application Protocol (WAP) to establish a wirelesscommunication channel. The communication module 572 may implement awireless networking standard such as Institute of Electrical andElectronics Engineers (IEEE) 802.11 standard, IEEE std. 802.11-1999,published by IEEE in 1999.

Examples of mobile computing systems may be a laptop computer, a tabletcomputer, a Netbook, a smart phone, a personal digital assistant, orother similar device with on board processing power and wirelesscommunications ability that is powered by a Direct Current (DC) powersource that supplies DC voltage to the mobile computing system and thatis solely within the mobile computing system and needs to be rechargedon a periodic basis, such as a fuel cell or a battery.

FIG. 6 shows a diagram of an example network environment that may beused with some of the described embodiments. Network environment 620includes computing systems 690 and 691. One or more of the computingsystems 690 and 691 may be a mobile computing system or a sensor thatmay be worn on a person's body. The computing systems 690 and 691 may beconnected to the network 650 via a cellular connection or via a Wi-Firouter (not shown). The network 650 may be the Internet. The computingsystems 690 and 691 may be coupled with server computing systems 655 and665 via the network 650.

Each of the computing systems 690 and 691 may include an applicationmodule such as module 608 or 614. For example, a user (e.g., adeveloper) may use the computing system 690 and the application module608 to connect to and communicate with the server computing system 655and log into application 657.

While one or more implementations have been described by way of exampleand in terms of the specific embodiments, it is to be understood thatone or more implementations are not limited to the disclosedembodiments. To the contrary, it is intended to cover variousmodifications and similar arrangements as would be apparent to thoseskilled in the art. Therefore, the scope of the appended claims shouldbe accorded the broadest interpretation so as to encompass all suchmodifications and similar arrangements.

1. A method comprising: receiving body ambient temperature data capturedby a sensor in contact with a person; characterizing one or more typesof activities performed by the person during a time range associatedwith the body ambient temperature data; and determining a riskclassification individualized for the person based at least on one ormore respective workloads and the corresponding allocations of work andrest experienced by the person during performance of the characterizedtypes of activities.
 2. The method of claim 1, wherein the body ambienttemperature data captured by a sensor comprises timestamped datarepresenting at least one of: body segment motion, body temperature,heart rate, galvanic skin response (GSR), electromyograms (EMG); andenvironmental conditions data comprising at least one of: humidity,pressure and positional information associated with a global navigationsatellite system.
 3. The method of claim of claim 1, whereincharacterizing one or more types of activities comprises: identifyingexternal data related to a geographic location of the person; andgenerating transformed data based on the body ambient temperature datasynchronized with the external data to represent risk conditionsassociated with one or more activities performed by the person duringthe time range of the body ambient temperature data.
 4. The method ofclaim 3, wherein the external data comprises at least one of: weatherdata and land survey data.
 5. The method of claim 3, wherein generatingtransformed data based on the body ambient temperature data synchronizedwith the external data comprises: trimming the external data to identifytrimmed external data contemporaneous to the time range of the bodyambient temperature data; generating synchronized data based onsynchronizing the trimmed external data and the body ambient temperaturedata; and transforming the synchronized data to generate transformeddata, the transformed data includes one or more of: (i) physicalorientation data of the body ambient temperature data transformed torepresentations of one or more body motions; (ii) positional informationof the body ambient temperature data combined with at least one of:accelerometer data, local ambient pressure from the external data,altitude data from the external data and motion data from the bodyambient temperature data converted into data representing a relationshipbetween physical energy burn and caloric consumption.
 6. The method ofclaim 1, wherein characterizing one or more types of activitiesperformed by the person during a time range associated with the bodyambient temperature data comprises: applying one or more patternrecognition techniques to transformed data based on external datasynchronized with the body ambient temperature data, the one or morepattern recognition techniques matching at least one or more segments oftime associated with the transformed data that is indicative of a timeepoch of a performed pre-defined activity available from a plurality ofpre-defined activities.
 7. The method of claim 6, wherein plurality ofpre-defined activities comprises: lifting an object, pushing an object,pulling an object, carrying an object, walking, running, standing,sitting, sitting in a moving vehicle and climbing stairs.
 8. The methodof claim 1, wherein determining a risk classification individualized forthe person comprises: identifying respective workloads associated withthe one or more types of characterized activities and one or moreallocations of work and rest when the person incurred each therespective workloads.
 9. The method of claim 8, wherein a respectiveworkload is based on an established metabolic rate experienced during apre-defined activity that matches a respective activity represented inpart by the body ambient temperature data; and wherein a respectiveallocation of work and rest describes a ratio of rest and non-restduring an interval of time in which the respective workload wasexperienced.
 10. A computer program product comprising computer-readableprogram code to be executed by one or more processors when retrievedfrom a non-transitory computer-readable medium, the program codeincluding at least one instruction to: receive body ambient temperaturedata captured by a sensor in contact with a person; characterize one ormore types of activities performed by the person during a time rangeassociated with the body ambient temperature data; and determine a riskclassification individualized for the person based at least on one ormore respective workloads and the corresponding allocations of work andrest experienced by the person during performance of the characterizedtypes of activities.
 11. The computer program product of claim 10,wherein the body ambient temperature data captured by a sensor comprisestimestamped data representing at least one of: body segment motion, bodytemperature, heart rate, galvanic skin response (GSR), electromyograms(EMG); and environmental conditions data comprising at least one of:humidity, pressure and positional information associated with a globalnavigation satellite system.
 12. The computer program product of claim10, wherein the program code to characterize one or more types ofactivities further includes instructions to: identify external datarelated to a geographic location of the person; and generate transformeddata based on the body ambient temperature data synchronized with theexternal data to represent risk conditions associated with one or moreactivities performed by the person during the time range of the bodyambient temperature data.
 13. The computer program product of claim 12,wherein the external data comprises at least one of: weather data andland survey data.
 14. The computer program product of claim 12, whereinthe program code to generate transformed data further includesinstructions to: trim the external data to identify trimmed externaldata contemporaneous to the time range of the body ambient temperaturedata; generate synchronized data based on synchronizing the trimmedexternal data and the body ambient temperature data; and transform thesynchronized data to generate transformed data, the transformed dataincludes one or more of: (i) physical orientation data of the bodyambient temperature data transformed to representations of one or morebody motions; (ii) positional information of the body ambienttemperature data combined with at least one of: accelerometer data,local ambient pressure from the external data, altitude data from theexternal data and motion data from the body ambient temperature dataconverted into data representing a relationship between physical energyburn and caloric consumption.
 15. The computer program product of claim10, wherein the program code to characterize one or more types ofactivities further includes instructions to: apply one or more patternrecognition techniques to transformed data based on external datasynchronized with the body ambient temperature data, the one or morepattern recognition techniques matching at least one or more segments oftime associated with the transformed data that is indicative of a timeepoch of a performed pre-defined activity available from a plurality ofpre-defined activities.
 16. The computer program product of claim 15,wherein plurality of pre-defined activities comprises: lifting anobject, pushing an object, pulling an object, carrying an object,walking, running, standing, sitting, sitting in a moving vehicle andclimbing stairs.
 17. The computer program product of claim 10, whereinthe program code to determine a risk classification individualized forthe person further includes instructions to: identify respectiveworkloads associated with the one or more types of characterizedactivities and one or more allocations of work and rest when the personincurred each the respective workloads.
 18. The computer program productof claim 17, wherein a respective workload is based on an establishedmetabolic rate experienced during a pre-defined activity that matches arespective activity represented in part by the body ambient temperaturedata; and wherein a respective allocation of work and rest describes aratio of rest and non-rest during an interval of time in which therespective workload was experienced.
 19. A system comprising: one ormore processors; and a non-transitory computer readable medium storing aplurality of instructions, which when executed, cause the one or moreprocessors to: receive body ambient temperature data captured by asensor in contact with a person; characterize one or more types ofactivities performed by the person during a time range associated withthe body ambient temperature data; and determine a risk classificationindividualized for the person based at least on one or more respectiveworkloads and the corresponding allocations of work and rest experiencedby the person during performance of the characterized types ofactivities.
 20. The system of claim 19, wherein the plurality ofinstructions, which when executed, cause the one or more processors tocharacterize one or more types of activities further includes one ormore instructions to: apply one or more pattern recognition techniquesto transformed data based on external data synchronized with the bodyambient temperature data, the one or more pattern recognition techniquesmatching at least one or more segments of time associated with thetransformed data that is indicative of a time epoch of a performedpre-defined activity available from a plurality of pre-definedactivities; and wherein the plurality of instructions, which whenexecuted, cause the one or more processors to determine a riskclassification further includes one or more instructions to: identifyrespective workloads associated with the one or more types ofcharacterized activities and one or more allocations of work and restwhen the person incurred each the respective workloads, wherein arespective workload is based on an established metabolic rateexperienced during a pre-defined activity that matches a respectiveactivity represented in part by the body ambient temperature data andwherein a respective allocation of work and rest describes a ratio ofrest and non-rest during an interval of time in which the respectiveworkload was experienced.